Project 1A Furniture Mart : Basic Sales Reporting.(Python/Pandas/matplotlib/ChatGPT)

Introduction

In this session, we delved into the fascinating and somewhat unfortunate news about the assassination of the CEO of United Health in New York City, which sparked curiosity and concern. Transitioning from that shocking event, we moved to a hands-on data analysis project involving Furniture Mart, focusing on basic sales reporting using Python.

Introduction to Data Reporting

The journey began with the analysis of sales data pertaining to Furniture Mart, where our main objectives included generating reports on total sales, total quantity, total profits, and a breakdown of sales by product and subcategory. We utilized Python, specifically the Pandas library, to manipulate and analyze the data efficiently.

Starting with Data Visualization

Initially, we employed data visualizations to better understand the trends over the years. A summary table displaying total sales by year and month was created, which highlighted critical insights. Using the itable library, we generated a summary table that focused on these sales figures along with detailed plots to visualize the sales distribution across different product subcategories.

Key Insights from Visualizations

We created several visualizations:

Yearly sales trends over the months.
Breakdown of sales by product categories such as furniture, books, etc.
An analysis that included color coding to represent different years in the plots, which highlighted shifts in consumer behavior and product popularity.

Detailed Analysis Approach

To ensure we captured all pertinent data, we implemented a few specific queries:

Generated a report summarizing total sales, profit, and discounts by shipping mode.
Created SQL-like queries in Python to provide better clarity and organization to our data analysis. This included grouping data and utilizing aggregate functions to streamline our findings.

Handling Data Manipulation

We faced some challenges along the way, like dealing with duplicate entries in our data. Through careful adjustments and re-running segments of code, we were able to refine our report further and eliminate these discrepancies.

Final Output and Reporting

After thorough analysis and data verification, we compiled all the findings into a cohesive report format, generating both visual and tabular data outputs. Additional formats, such as PDF, were considered to enhance the accessibility of our findings.

As we concluded the session, we noted the critical takeaway points:

The significance of data analysis in understanding sales performance.
The insights gained from visual representations of data.
Importance of iterative testing and refinement of code in Pandas.

With this project approach, we prepare to transition into more complex analyses in the future.

Keywords

Python
Pandas
Data Analysis
Sales Reporting
Data Visualization
SQL Queries
Shipping Mode

FAQ

Q: What is the main focus of this project? A: The main focus is on basic sales reporting for Furniture Mart, utilizing Python and Pandas for data analysis and visualization.

Q: Which libraries are used in the analysis? A: We primarily used Python's Pandas for data manipulation and Matplotlib for data visualization.

Q: How can I generate visual representations of data in Python? A: You can use the Matplotlib library in Python to create various types of plots and charts to visualize your data clearly.

Q: What challenges did you face in the project? A: The main challenges included handling duplicate data entries and ensuring the integrity of the reports generated.

Q: How was the final report structured? A: The final report included summary tables, detailed visualizations, insights into sales trends, and formatted outputs, including PDF options for distribution.